Subseasonal-to-seasonal (S2S) prediction of winter wheat yields is crucial for farmers and decision-makers to reduce yield losses and ensure food security. Recently, numerous researchers have utilized machine learning (ML) methods to predict crop yield, using observational climate variables and satellite data. Meanwhile, some studies also illustrated the potential of state-of-the-art dynamical atmospheric prediction in crop yield forecasting. However, the potential of coupling both methods has not been fully explored. Herein, we aimed to establish a skilled ML–dynamical hybrid model for crop yield forecasting (MHCF v1.0), which hybridizes ML and a global dynamical atmospheric prediction system, and applied it to northern China at the S2S time scale. In this study, we adopted three mainstream machining learning algorithms (XGBoost, RF, and SVR) and the multiple linear regression (MLR) model, and three major datasets, including satellite data from MOD13C1, observational climate data from CRU, and S2S atmospheric prediction data from IAP CAS, used to predict winter wheat yield from 2005 to 2014, at the grid level. We found that, among the four models examined in this work, XGBoost reached the highest skill with the S2S prediction as inputs, scoring R² of 0.85 and RMSE of 0.78 t/ha 3–4 months, leading the winter wheat harvest. Moreover, the results demonstrated that crop yield forecasting with S2S dynamical predictions generally outperforms that with observational climate data. Our findings highlighted that the coupling of ML and S2S dynamical atmospheric prediction provided a useful tool for yield forecasting, which could guide agricultural practices, policy-making and agricultural insurance.

This study systematically evaluated global tropical cyclone (TC) activity in a new global atmospheric reanalysis dataset named the “40‐year Global Reanalysis” (CRA40) against the best track data. For comparison, four state‐of‐the‐art reanalyses—ERA5, JRA55, CFSR, and MERRA2—were also assessed. The results showed that there is a general underestimation of global TC genesis frequency and intensity in both CRA40 and other reanalyses. A detailed investigation of spatial distribution, seasonality, interannual variation, and long‐term trend for TC genesis frequency, as well as pressure–wind relationship for TC intensity, revealed similarities and differences among these reanalyses datasets. Overall, CRA40 does not exhibit clear advantages over other reanalyses in these aspects, but its biases are also not more pronounced. However, regarding TC translation speed, CRA40 outpeforms other reanalyses, evident by its high level of consistency with the observation in the zonal average pattern, meridional distribution at peak latitudes, and interannual variation, suggesting its reasonable capability in capturing large‐scale atmospheric characteristics. Our findings indicate that the use of CRA40 is appropriate for conducting TC‐related studies, within the scope of its limitations.

Based on 35-year (1981–2015) ensemble (24 members) hindcasts of the IAP/LASG global seasonal prediction system named FGOALS-f2 V1.0 (FGOALS-f2), the tropical cyclone (TC) seasonal prediction skills over the South China Sea (SCS) during the TC peak season (July–November) are evaluated. Starting the prediction from June 20th, FGOALS-f2 can well capture the seasonal mean characteristics for both the genesis location and track of TCs over the SCS. For seasonal anomalous TC numbers, FGOALS-f2 underestimates the maximum and minimum of the TC number compared to the observation. The temporal correlation coefficients (TCCs) between FGOALS-f2 and the observation are 0.39 for the TC number and 0.51 for accumulated cyclone energy (ACE) over the SCS, respectively, which are both above the 95% significant level. Additionally, FGOALS-f2 has acceptable prediction skill for the seasonal mean number of TCs landing on three areas (coastal southeastern China, Indochina Peninsula, and Philippines) surrounding the SCS. The skillful prediction of SCS TCs could be ascribed to the well-predicted tropical anomaly of sea surface temperature (SSTA), TC and El Niño-Southern Oscillation (TC-ENSO) relations, and Genesis potential index (GPI).

The trade winds cause strong upwelling in the eastern equatorial Pacific, and create the eastern Pacific Cold Tongue (EPCT) that has far‐reaching impacts on global climate. However, large discrepancies persist in quantifying 20th‐century EPCT sea surface temperature (SST) changes across different instrumental data sets. Here we synthesize four coral Sr/Ca‐SST records from the tropical central‐eastern Pacific to develop a Cold Tongue Index (CTI) reconstruction for 1887–1997. The coral CTI record shows a rapid 20th century warming of the EPCT, suggesting an underestimation of warming trends in instrumental CTI records. The decadal to multidecadal changes in reconstructed EPCT SST show an association with the Walker Circulation. Our reconstruction indicates that recent EPCT cooling during the global warming hiatus is not unusual in the context of the 20th century. Our results provide new evidence for 20th century EPCT SST changes and an observational constraint for predicting future tropical climate changes.

This study investigates the impact of numerical accuracy of terrain slope on advection errors under Gal‐Chen and Somerville terrain‐following coordinates. Through a series of idealized 2D linear advection experiments, we demonstrate that increasing the numerical accuracy of terrain slope (from second‐ to fourth‐order) amplifies advection errors under steep topographic conditions, irrespective of the numerical accuracy of other spatial differences in the advection equation. Mechanistic analysis reveals that this error amplification is predominantly caused by the horizontal advection process, wherein higher‐order accuracy terrain slope introduces intensified unresolved small‐scale topographic forcings, which manifest as spurious waves through aliasing. These findings indicate that increased numerical accuracy does not invariably enhance model performance and highlight the need for careful attention to terrain representation in the model.